Overview

Dataset statistics

Number of variables13
Number of observations151
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.5 KiB
Average record size in memory104.8 B

Variable types

Categorical1
Numeric12

Alerts

Country,Other has a high cardinality: 151 distinct values High cardinality
Total Cases is highly correlated with New Cases and 7 other fieldsHigh correlation
New Cases is highly correlated with Total Cases and 7 other fieldsHigh correlation
Total Deaths is highly correlated with Total Cases and 7 other fieldsHigh correlation
New Deaths is highly correlated with Total Cases and 7 other fieldsHigh correlation
Total Recovered is highly correlated with Total Cases and 7 other fieldsHigh correlation
New Recovered is highly correlated with Total Cases and 7 other fieldsHigh correlation
Active Cases is highly correlated with Total Cases and 7 other fieldsHigh correlation
Serious, Critical is highly correlated with Total Cases and 7 other fieldsHigh correlation
Tot Cases/ 1M pop is highly correlated with Deaths/ 1M pop and 1 other fieldsHigh correlation
Deaths/ 1M pop is highly correlated with Tot Cases/ 1M pop and 1 other fieldsHigh correlation
Total Tests is highly correlated with Total Cases and 7 other fieldsHigh correlation
Tests/ 1M pop is highly correlated with Tot Cases/ 1M pop and 1 other fieldsHigh correlation
Total Cases is highly correlated with New Cases and 6 other fieldsHigh correlation
New Cases is highly correlated with Total Cases and 6 other fieldsHigh correlation
Total Deaths is highly correlated with Total Cases and 6 other fieldsHigh correlation
New Deaths is highly correlated with Total Cases and 6 other fieldsHigh correlation
Total Recovered is highly correlated with Total Cases and 6 other fieldsHigh correlation
New Recovered is highly correlated with Total Cases and 6 other fieldsHigh correlation
Active Cases is highly correlated with Total Cases and 6 other fieldsHigh correlation
Serious, Critical is highly correlated with Total Cases and 6 other fieldsHigh correlation
Tot Cases/ 1M pop is highly correlated with Deaths/ 1M popHigh correlation
Deaths/ 1M pop is highly correlated with Tot Cases/ 1M popHigh correlation
Total Cases is highly correlated with New Cases and 7 other fieldsHigh correlation
New Cases is highly correlated with Total Cases and 7 other fieldsHigh correlation
Total Deaths is highly correlated with Total Cases and 5 other fieldsHigh correlation
New Deaths is highly correlated with Total Cases and 4 other fieldsHigh correlation
Total Recovered is highly correlated with Total Cases and 6 other fieldsHigh correlation
New Recovered is highly correlated with Total Cases and 2 other fieldsHigh correlation
Active Cases is highly correlated with Total Cases and 1 other fieldsHigh correlation
Serious, Critical is highly correlated with Total Cases and 4 other fieldsHigh correlation
Tot Cases/ 1M pop is highly correlated with Deaths/ 1M pop and 1 other fieldsHigh correlation
Deaths/ 1M pop is highly correlated with Tot Cases/ 1M popHigh correlation
Total Tests is highly correlated with Total Cases and 3 other fieldsHigh correlation
Tests/ 1M pop is highly correlated with Tot Cases/ 1M popHigh correlation
Total Cases is highly correlated with New Cases and 7 other fieldsHigh correlation
New Cases is highly correlated with Total Cases and 7 other fieldsHigh correlation
Total Deaths is highly correlated with Total Cases and 7 other fieldsHigh correlation
New Deaths is highly correlated with Total Cases and 7 other fieldsHigh correlation
Total Recovered is highly correlated with Total Cases and 7 other fieldsHigh correlation
New Recovered is highly correlated with Total Cases and 6 other fieldsHigh correlation
Active Cases is highly correlated with Total Cases and 7 other fieldsHigh correlation
Serious, Critical is highly correlated with Total Cases and 7 other fieldsHigh correlation
Tot Cases/ 1M pop is highly correlated with Deaths/ 1M pop and 1 other fieldsHigh correlation
Deaths/ 1M pop is highly correlated with Tot Cases/ 1M popHigh correlation
Total Tests is highly correlated with Total Cases and 7 other fieldsHigh correlation
Tests/ 1M pop is highly correlated with Tot Cases/ 1M pop and 1 other fieldsHigh correlation
Country,Other is uniformly distributed Uniform
Country,Other has unique values Unique
Total Cases has unique values Unique
Tot Cases/ 1M pop has unique values Unique

Reproduction

Analysis started2022-01-15 09:44:07.786330
Analysis finished2022-01-15 09:45:43.246670
Duration1 minute and 35.46 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Country,Other
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct151
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Vietnam
 
1
Honduras
 
1
Costa Rica
 
1
Laos
 
1
Botswana
 
1
Other values (146)
146 

Length

Max length22
Median length7
Mean length7.635761589
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)100.0%

Sample

1st rowWorld
2nd rowUSA
3rd rowIndia
4th rowBrazil
5th rowUK

Common Values

ValueCountFrequency (%)
Vietnam1
 
0.7%
Honduras1
 
0.7%
Costa Rica1
 
0.7%
Laos1
 
0.7%
Botswana1
 
0.7%
Togo1
 
0.7%
Belgium1
 
0.7%
Seychelles1
 
0.7%
Moldova1
 
0.7%
Guinea1
 
0.7%
Other values (141)141
93.4%

Length

2022-01-15T15:15:43.686908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and2
 
1.2%
french2
 
1.2%
guinea2
 
1.2%
venezuela1
 
0.6%
korea1
 
0.6%
albania1
 
0.6%
maldives1
 
0.6%
greece1
 
0.6%
bangladesh1
 
0.6%
palestine1
 
0.6%
Other values (157)157
92.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Total Cases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct151
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4242925.285
Minimum31098
Maximum320721656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-01-15T15:15:44.124440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum31098
5-th percentile36093.5
Q1120977.5
median451430
Q31468419.5
95-th percentile9213442.5
Maximum320721656
Range320690558
Interquartile range (IQR)1347442

Descriptive statistics

Standard deviation26756833.5
Coefficient of variation (CV)6.306223114
Kurtosis132.9500091
Mean4242925.285
Median Absolute Deviation (MAD)389040
Skewness11.272381
Sum640681718
Variance7.159281389 × 1014
MonotonicityStrictly decreasing
2022-01-15T15:15:44.530659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5936641
 
0.7%
23471641
 
0.7%
67931191
 
0.7%
79305281
 
0.7%
4898891
 
0.7%
2265981
 
0.7%
925811
 
0.7%
13945991
 
0.7%
5400721
 
0.7%
4438171
 
0.7%
Other values (141)141
93.4%
ValueCountFrequency (%)
310981
0.7%
316041
0.7%
325221
0.7%
338881
0.7%
352021
0.7%
354251
0.7%
355321
0.7%
357551
0.7%
364321
0.7%
398901
0.7%
ValueCountFrequency (%)
3207216561
0.7%
652364751
0.7%
365821291
0.7%
228158271
0.7%
149678171
0.7%
132403041
0.7%
107233051
0.7%
102712401
0.7%
81556451
0.7%
79305281
0.7%

New Cases
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct134
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42599.12583
Minimum-1
Maximum3220758
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)10.6%
Memory size1.3 KiB
2022-01-15T15:15:44.983814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1584
median2312
Q39500.5
95-th percentile118767.5
Maximum3220758
Range3220759
Interquartile range (IQR)8916.5

Descriptive statistics

Standard deviation271627.5898
Coefficient of variation (CV)6.376365348
Kurtosis127.4270648
Mean42599.12583
Median Absolute Deviation (MAD)2257
Skewness10.98167801
Sum6432468
Variance7.378154752 × 1010
MonotonicityNot monotonic
2022-01-15T15:15:45.390028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-116
 
10.6%
6742
 
1.3%
5772
 
1.3%
91221
 
0.7%
393211
 
0.7%
17521
 
0.7%
441871
 
0.7%
1281
 
0.7%
10471
 
0.7%
4231
 
0.7%
Other values (124)124
82.1%
ValueCountFrequency (%)
-116
10.6%
31
 
0.7%
341
 
0.7%
531
 
0.7%
551
 
0.7%
1121
 
0.7%
1281
 
0.7%
1691
 
0.7%
1721
 
0.7%
1901
 
0.7%
ValueCountFrequency (%)
32207581
0.7%
8064931
0.7%
3053221
0.7%
2642021
0.7%
1846151
0.7%
1591611
0.7%
1539681
0.7%
1284021
0.7%
1091331
0.7%
972211
0.7%

Total Deaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct150
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73268.60265
Minimum38
Maximum5538897
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-01-15T15:15:45.827533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile283
Q11604
median6035
Q321429.5
95-th percentile147748.5
Maximum5538897
Range5538859
Interquartile range (IQR)19825.5

Descriptive statistics

Standard deviation459649.49
Coefficient of variation (CV)6.273485141
Kurtosis135.7398778
Mean73268.60265
Median Absolute Deviation (MAD)5412
Skewness11.40987237
Sum11063559
Variance2.112776536 × 1011
MonotonicityNot monotonic
2022-01-15T15:15:46.218156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25142
 
1.3%
18531
 
0.7%
104161
 
0.7%
393311
 
0.7%
17001
 
0.7%
242291
 
0.7%
9311
 
0.7%
24741
 
0.7%
19671
 
0.7%
12011
 
0.7%
Other values (140)140
92.7%
ValueCountFrequency (%)
381
0.7%
431
0.7%
1221
0.7%
1361
0.7%
1861
0.7%
2611
0.7%
2641
0.7%
2691
0.7%
2971
0.7%
3451
0.7%
ValueCountFrequency (%)
55388971
0.7%
8692121
0.7%
6206091
0.7%
4853501
0.7%
3191721
0.7%
3007641
0.7%
2032551
0.7%
1513421
0.7%
1441551
0.7%
1401881
0.7%

New Deaths
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.5430464
Minimum-1
Maximum7691
Zeros0
Zeros (%)0.0%
Negative35
Negative (%)23.2%
Memory size1.3 KiB
2022-01-15T15:15:46.698359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11
median6
Q322.5
95-th percentile243
Maximum7691
Range7692
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation648.0327845
Coefficient of variation (CV)6.381852896
Kurtosis127.9634224
Mean101.5430464
Median Absolute Deviation (MAD)7
Skewness11.02062268
Sum15333
Variance419946.4898
MonotonicityNot monotonic
2022-01-15T15:15:47.120229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-135
23.2%
311
 
7.3%
210
 
6.6%
19
 
6.0%
46
 
4.0%
75
 
3.3%
54
 
2.6%
84
 
2.6%
104
 
2.6%
134
 
2.6%
Other values (47)59
39.1%
ValueCountFrequency (%)
-135
23.2%
19
 
6.0%
210
 
6.6%
311
 
7.3%
46
 
4.0%
54
 
2.6%
61
 
0.7%
75
 
3.3%
84
 
2.6%
92
 
1.3%
ValueCountFrequency (%)
76911
0.7%
19691
0.7%
7401
0.7%
4801
0.7%
3351
0.7%
3161
0.7%
3151
0.7%
2611
0.7%
2251
0.7%
2061
0.7%

Total Recovered
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct149
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3478926.53
Minimum-1
Maximum264053767
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)2.0%
Memory size1.3 KiB
2022-01-15T15:15:47.620229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile9457
Q180824
median368835
Q31223236
95-th percentile7867777.5
Maximum264053767
Range264053768
Interquartile range (IQR)1142412

Descriptive statistics

Standard deviation21934819.16
Coefficient of variation (CV)6.305053864
Kurtosis135.2338001
Mean3478926.53
Median Absolute Deviation (MAD)328228
Skewness11.38757718
Sum525317906
Variance4.811362916 × 1014
MonotonicityNot monotonic
2022-01-15T15:15:48.448359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-13
 
2.0%
11555051
 
0.7%
7256841
 
0.7%
9614621
 
0.7%
4584251
 
0.7%
455221
 
0.7%
9665881
 
0.7%
2268281
 
0.7%
22501
 
0.7%
307091
 
0.7%
Other values (139)139
92.1%
ValueCountFrequency (%)
-13
2.0%
1041
 
0.7%
7731
 
0.7%
22501
 
0.7%
29641
 
0.7%
76601
 
0.7%
112541
 
0.7%
195241
 
0.7%
254161
 
0.7%
281901
 
0.7%
ValueCountFrequency (%)
2640537671
0.7%
429114901
0.7%
348247061
0.7%
216501511
0.7%
111269531
0.7%
97843481
0.7%
94883371
0.7%
88574551
0.7%
68781001
0.7%
60585861
0.7%

New Recovered
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct119
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15426.91391
Minimum-1
Maximum1166443
Zeros0
Zeros (%)0.0%
Negative32
Negative (%)21.2%
Memory size1.3 KiB
2022-01-15T15:15:48.885859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q188
median751
Q34058
95-th percentile47638
Maximum1166443
Range1166444
Interquartile range (IQR)3970

Descriptive statistics

Standard deviation96345.79929
Coefficient of variation (CV)6.245306084
Kurtosis138.303512
Mean15426.91391
Median Absolute Deviation (MAD)752
Skewness11.54079059
Sum2329464
Variance9282513041
MonotonicityNot monotonic
2022-01-15T15:15:49.401515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-132
 
21.2%
7922
 
1.3%
1064001
 
0.7%
938781
 
0.7%
1811
 
0.7%
9501
 
0.7%
1751
 
0.7%
8541
 
0.7%
14561
 
0.7%
260311
 
0.7%
Other values (109)109
72.2%
ValueCountFrequency (%)
-132
21.2%
41
 
0.7%
141
 
0.7%
261
 
0.7%
381
 
0.7%
731
 
0.7%
771
 
0.7%
991
 
0.7%
1001
 
0.7%
1111
 
0.7%
ValueCountFrequency (%)
11664431
0.7%
1093451
0.7%
1064001
0.7%
938781
0.7%
891731
0.7%
828031
0.7%
801881
0.7%
516751
0.7%
436011
0.7%
415001
0.7%

Active Cases
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct149
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean671683.351
Minimum-1
Maximum51128992
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)2.0%
Memory size1.3 KiB
2022-01-15T15:15:49.901482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2153.5
Q18500
median33726
Q3108131
95-th percentile917875.5
Maximum51128992
Range51128993
Interquartile range (IQR)99631

Descriptive statistics

Standard deviation4515256.833
Coefficient of variation (CV)6.722299765
Kurtosis108.2641392
Mean671683.351
Median Absolute Deviation (MAD)28240
Skewness10.09094081
Sum101424186
Variance2.038754427 × 1013
MonotonicityNot monotonic
2022-01-15T15:15:50.427291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-13
 
2.0%
511289921
 
0.7%
67851
 
0.7%
349801
 
0.7%
3859601
 
0.7%
769691
 
0.7%
165541
 
0.7%
311491
 
0.7%
1094861
 
0.7%
8111831
 
0.7%
Other values (139)139
92.1%
ValueCountFrequency (%)
-13
2.0%
821
 
0.7%
6461
 
0.7%
11521
 
0.7%
14251
 
0.7%
17631
 
0.7%
25441
 
0.7%
26701
 
0.7%
32621
 
0.7%
34601
 
0.7%
ValueCountFrequency (%)
511289921
0.7%
214557731
0.7%
42563191
0.7%
36895221
0.7%
26070241
0.7%
23235181
0.7%
12720731
0.7%
9410951
0.7%
8946561
0.7%
8111831
0.7%

Serious, Critical
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct102
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1277.350993
Minimum-1
Maximum96547
Zeros0
Zeros (%)0.0%
Negative24
Negative (%)15.9%
Memory size1.3 KiB
2022-01-15T15:15:51.187533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q110
median61
Q3356.5
95-th percentile2809
Maximum96547
Range96548
Interquartile range (IQR)346.5

Descriptive statistics

Standard deviation8159.604257
Coefficient of variation (CV)6.38791084
Kurtosis126.4194333
Mean1277.350993
Median Absolute Deviation (MAD)62
Skewness10.93504382
Sum192880
Variance66579141.63
MonotonicityNot monotonic
2022-01-15T15:15:51.890662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-124
 
15.9%
234
 
2.6%
113
 
2.0%
673
 
2.0%
133
 
2.0%
603
 
2.0%
493
 
2.0%
222
 
1.3%
62
 
1.3%
102
 
1.3%
Other values (92)102
67.5%
ValueCountFrequency (%)
-124
15.9%
12
 
1.3%
42
 
1.3%
52
 
1.3%
62
 
1.3%
72
 
1.3%
82
 
1.3%
91
 
0.7%
102
 
1.3%
113
 
2.0%
ValueCountFrequency (%)
965471
0.7%
252741
0.7%
89441
0.7%
83181
0.7%
60061
0.7%
47981
0.7%
39851
0.7%
32121
0.7%
24061
0.7%
23001
0.7%

Tot Cases/ 1M pop
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct151
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86029.38411
Minimum73
Maximum316557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-01-15T15:15:52.625036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile2827
Q116621
median73432
Q3136085.5
95-th percentile209153.5
Maximum316557
Range316484
Interquartile range (IQR)119464.5

Descriptive statistics

Standard deviation72934.00563
Coefficient of variation (CV)0.847780167
Kurtosis-0.06091695134
Mean86029.38411
Median Absolute Deviation (MAD)57506
Skewness0.7430429415
Sum12990437
Variance5319369177
MonotonicityNot monotonic
2022-01-15T15:15:53.250039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109111
 
0.7%
142281
 
0.7%
47631
 
0.7%
1535031
 
0.7%
505951
 
0.7%
168061
 
0.7%
321681
 
0.7%
790191
 
0.7%
954051
 
0.7%
2021581
 
0.7%
Other values (141)141
93.4%
ValueCountFrequency (%)
731
0.7%
8721
0.7%
10991
0.7%
11681
0.7%
18801
0.7%
25741
0.7%
26891
0.7%
27871
0.7%
28671
0.7%
28761
0.7%
ValueCountFrequency (%)
3165571
0.7%
3132351
0.7%
2466111
0.7%
2461981
0.7%
2385851
0.7%
2187241
0.7%
2112821
0.7%
2095901
0.7%
2087171
0.7%
2021581
0.7%

Deaths/ 1M pop
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct147
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1181.341722
Minimum3
Maximum6036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2022-01-15T15:15:53.875038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile40.5
Q1213.5
median826
Q31893
95-th percentile3311.5
Maximum6036
Range6033
Interquartile range (IQR)1679.5

Descriptive statistics

Standard deviation1144.095003
Coefficient of variation (CV)0.9684708347
Kurtosis1.850239325
Mean1181.341722
Median Absolute Deviation (MAD)684
Skewness1.303098559
Sum178382.6
Variance1308953.377
MonotonicityNot monotonic
2022-01-15T15:15:54.437538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32
 
1.3%
652
 
1.3%
302
 
1.3%
1422
 
1.3%
631
 
0.7%
3871
 
0.7%
15301
 
0.7%
14971
 
0.7%
7951
 
0.7%
1851
 
0.7%
Other values (137)137
90.7%
ValueCountFrequency (%)
32
1.3%
131
0.7%
141
0.7%
271
0.7%
302
1.3%
391
0.7%
421
0.7%
441
0.7%
541
0.7%
601
0.7%
ValueCountFrequency (%)
60361
0.7%
46481
0.7%
42231
0.7%
41741
0.7%
39161
0.7%
38741
0.7%
36101
0.7%
34251
0.7%
31981
0.7%
31351
0.7%

Total Tests
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct149
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31801364.92
Minimum-1
Maximum852902599
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)2.0%
Memory size1.3 KiB
2022-01-15T15:15:55.078163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile295247.5
Q11316301.5
median3941654
Q319424123.5
95-th percentile126830392.5
Maximum852902599
Range852902600
Interquartile range (IQR)18107822

Descriptive statistics

Standard deviation100454583.4
Coefficient of variation (CV)3.15881358
Kurtosis43.23203038
Mean31801364.92
Median Absolute Deviation (MAD)3482115
Skewness6.201962597
Sum4802006103
Variance1.009112332 × 1016
MonotonicityNot monotonic
2022-01-15T15:15:55.845957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-13
 
2.0%
637761661
 
0.7%
27432111
 
0.7%
117782211
 
0.7%
8467041
 
0.7%
478407101
 
0.7%
169896161
 
0.7%
413733641
 
0.7%
19199251
 
0.7%
9391901
 
0.7%
Other values (139)139
92.1%
ValueCountFrequency (%)
-13
2.0%
1462691
 
0.7%
1769191
 
0.7%
1884801
 
0.7%
2308611
 
0.7%
2491491
 
0.7%
3413461
 
0.7%
3457421
 
0.7%
3621171
 
0.7%
3995551
 
0.7%
ValueCountFrequency (%)
8529025991
0.7%
6973116271
0.7%
4286823951
0.7%
2449000001
0.7%
2075330891
0.7%
1600000001
0.7%
1525192121
0.7%
1297722581
0.7%
1238885271
0.7%
1163290341
0.7%

Tests/ 1M pop
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct149
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1380452.887
Minimum-1
Maximum19241137
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)2.0%
Memory size1.3 KiB
2022-01-15T15:15:56.348792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile15389
Q1162051.5
median601256
Q31453721.5
95-th percentile4858315.5
Maximum19241137
Range19241138
Interquartile range (IQR)1291670

Descriptive statistics

Standard deviation2463402.455
Coefficient of variation (CV)1.78448861
Kurtosis24.17113076
Mean1380452.887
Median Absolute Deviation (MAD)533937
Skewness4.353010698
Sum208448386
Variance6.068351654 × 1012
MonotonicityNot monotonic
2022-01-15T15:15:56.817543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-13
 
2.0%
7699551
 
0.7%
1263581
 
0.7%
6012561
 
0.7%
12918151
 
0.7%
16206531
 
0.7%
25537751
 
0.7%
192411371
 
0.7%
6254051
 
0.7%
31686911
 
0.7%
Other values (139)139
92.1%
ValueCountFrequency (%)
-13
2.0%
51231
 
0.7%
80581
 
0.7%
90341
 
0.7%
118601
 
0.7%
123951
 
0.7%
183831
 
0.7%
207031
 
0.7%
256881
 
0.7%
270621
 
0.7%
ValueCountFrequency (%)
192411371
0.7%
142839991
0.7%
115480791
0.7%
77628841
0.7%
65847731
0.7%
62643091
0.7%
60461351
0.7%
50043951
0.7%
47122361
0.7%
46510241
0.7%

Interactions

2022-01-15T15:15:33.702085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:09.799230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:15.575209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:20.416381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:24.993145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:43.289806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:51.227207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:00.718573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:08.251952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:16.270233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:23.072277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:28.555359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:34.352411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:10.277324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:15.979993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:20.803550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:25.416764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:44.350615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:51.789704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:01.899821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:08.774555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:17.074039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:23.576236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:28.919053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:35.017212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:10.869728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:16.388089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:21.187816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:25.903450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:45.024836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:52.227209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:02.522615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:09.440687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:17.851819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:24.070631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:29.302556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:35.700608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:11.262248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:16.763778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:21.564199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:26.553059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:45.685948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:53.076435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:03.054132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:10.020343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:18.382849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:24.491886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:29.661662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:36.291772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:11.854985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:17.193872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:21.978054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:27.091248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:46.413953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:53.895671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:03.641398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:10.705812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:18.869681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:24.993372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:30.049866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:36.876931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:12.292458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:17.580868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:22.345426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:36.371198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:47.013951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:55.671840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:04.237852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:11.325944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:19.389533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:25.442231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:30.409117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:37.826089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:12.803578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:17.989798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:22.718726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:37.708396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:47.837952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:56.715230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:04.843577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:11.951344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:19.868255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:25.928413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:30.784608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:38.317133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:13.229158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:18.413275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:23.080762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:38.370173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:48.537131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:57.586021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:05.318944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:12.629756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:20.332039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:26.557260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:31.159394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:38.859872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:13.609075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:18.806704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:23.448674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:38.954732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:49.021510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:58.250804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:05.793808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:13.457137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:20.785247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:26.943674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:31.516456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:39.422466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:14.352086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:19.204364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:23.837804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:39.575078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:49.505885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:58.907429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:06.388603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:14.220206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:21.257549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:27.350176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:32.156360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:39.980593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:14.749629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:19.614378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:24.212437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:41.121804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:50.177755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:59.517829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:07.007491image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:15.019723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:22.111279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:27.759673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:32.667037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:40.540347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:15.123636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:19.975501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:24.567845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:42.209807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:14:50.615259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:00.051432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:07.574010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:15.621901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:22.545218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:28.112614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-15T15:15:33.054243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-01-15T15:15:57.651595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-15T15:15:58.714724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-15T15:15:59.898005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-15T15:16:00.959301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-15T15:15:41.452786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-15T15:15:42.820730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Country,OtherTotal CasesNew CasesTotal DeathsNew DeathsTotal RecoveredNew RecoveredActive CasesSerious, CriticalTot Cases/ 1M popDeaths/ 1M popTotal TestsTests/ 1M pop
0World3207216563220758553889776912640537671166443511289929654741146710.6-1-1
1USA6523647580649386921219694291149010640021455773252741953322603.08529025992553775
2India36582129264202485350315348247061093451272073894426115346.0697311627497798
3Brazil228158279722162060919021650151-154506783181061822888.063776166296806
4UK14967817109133151342335111269538917336895227852187242212.04286823956264309
5France13240304305322126530225885745593878425631939852021581932.02075330893168691
6Russia10723305211553191727409784348259846197852300734322186.02449000001677048
7Turkey1027124075564842781539488337516756986251128119810983.01238885271445112
8Italy8155645184615140188316569193982803232351816681351942324.01525192122528269
9Spain793052815916190620112523288416476260702422271695191937.0662138581415354

Last rows

Country,OtherTotal CasesNew CasesTotal DeathsNew DeathsTotal RecoveredNew RecoveredActive CasesSerious, CriticalTot Cases/ 1M popDeaths/ 1M popTotal TestsTests/ 1M pop
141Belize39890940607-13213937971444976071485.04154771016632
142Papua New Guinea364323595-135755-1827395765.024914927062
143Burundi35755-138-1773-134944-128763.034574227810
144Channel Islands35532406122-1307097354701-1201522692.011610146584773
145Togo354253282611281903226974-1413130.064124174775
146Guinea3520216940633092973386749257430.057821442274
147Barbados338885772692293381754281-1117702934.04954821720938
148Mayotte32522-1186-12964-129372-1114965658.0176919625405
149Lesotho31604128687-11952423011393-114576317.0362117167007
150Seychelles3109820681362254165125546-13132351370.0-1-1